Improvement of chatbots semantics using wit.ai and word sequence kernel: education chatbot as a case study

Designing interactive question-response systems has become an important challenge in Artificial Intelligence which aims to build a computer program, referred to as Chatbot, able to manage an online human-computer conversation with natural language. The number of chatbots continues to increase these recent years using different languages, tools and platforms and has been used in several domains, such as marketing, education and medicine. One of the most important issues in designing chatbots is its degree of interoperability with human natural language. In this context, we propose in this work a new messenger chatbot design approach based on Wit.ai and Word Sequence kernel in order to improve semantics. Wit.ai is used to detect contexts and concepts while the Word Sequence Kernel is used as a similarity measure between textual conversations taking into account the order of appearance of words in the conversation. A testing educate chatbot has been build which aims to provide FAQBot system for university students and acts as undergraduate advisor in student information desk. The performance of the proposed chatbot was compared to conventional messenger chatbots and showed better results.